摘要
Current multi-operator image resizing methods succeed in generating impressive results by using image similarity measure to guide the resizing process. An optimal operation path is found in the resizing space. However, their slow resizing speed caused by inefficient computation strategy of the bidirectional patch matching becomes a drawback in practical use. In this paper, we present a novel method to address this problem. By combining seam carving with scaling and cropping, our method can realize content-aware image resizing very fast. We define cost functions combing image energy and dominant color descriptor for all the operators to evaluate the damage to both local image content and global visual effect. Therefore our algorithm can automatically find an optimal sequence of operations to resize the image by using dynamic programming or greedy algorithm. We also extend our algorithm to indirect image resizing which can protect the aspect ratio of the dominant object in an image.
Current multi-operator image resizing methods succeed in generating impressive results by using image similarity measure to guide the resizing process. An optimal operation path is found in the resizing space. However, their slow resizing speed caused by inefficient computation strategy of the bidirectional patch matching becomes a drawback in practical use. In this paper, we present a novel method to address this problem. By combining seam carving with scaling and cropping, our method can realize content-aware image resizing very fast. We define cost functions combing image energy and dominant color descriptor for all the operators to evaluate the damage to both local image content and global visual effect. Therefore our algorithm can automatically find an optimal sequence of operations to resize the image by using dynamic programming or greedy algorithm. We also extend our algorithm to indirect image resizing which can protect the aspect ratio of the dominant object in an image.
作者
Wei-Ming Dong
Guan-Bo Bao
Xiao-Peng Zhang
Jean-Claude Paul
董未名;鲍冠伯;张晓鹏;Jean-Claude Paul(Sino-French Laboratory for Computer Science,Automation and Applied Mathematics/National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;National Institute for Research in Computer Science and Control,Domaine de Voluceau Rocquencourt Le Chesnay 78153,France)
基金
supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60872120, 60902078, 61172104
the Natural Science Foundation of Beijing under Grant No. 4112061
the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry of China
the French System@tic Paris-Region (CSDL Project)
the National Agency for Research of French (ANR)-NSFC under Grant No. 60911130368
